首页|Department of Gastroenterology Reports Findings in Personalized Medicine (Deciphering complex antibiotic resistance patterns in Helicobacter pylori through whole genome sequencing and machine learning)
Department of Gastroenterology Reports Findings in Personalized Medicine (Deciphering complex antibiotic resistance patterns in Helicobacter pylori through whole genome sequencing and machine learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Drugs and Therapies - Personalized Medicine is the subject of a report. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “Helicobacter pylori (H.pylori, Hp) affects billions of people worldwide. However, the emerging resistance of Hp to antibiotics challenges the effectiveness of current treatments.” Our news journalists obtained a quote from the research from the Department of Gastroenterology, “In- vestigating the genotype-phenotype connection for Hp using next-generation sequencing could enhance our understanding of this resistance. In this study, we analyzed 52 Hp strains collected from various hospitals. The susceptibility of these strains to five antibiotics was assessed using the agar dilution assay. Whole- genome sequencing was then performed to screen the antimicrobial resistance (AMR) genotypes of these Hp strains. To model the relationship between drug resistance and genotype, we employed univariate statistical tests, unsupervised machine learning, and supervised machine learning techniques, including the develop- ment of support vector machine models. Our models for predicting Amoxicillin resistance demonstrated 66% sensitivity and 100% specificity, while those for Clarithromycin resistance showed 100% sensitivity and 100% specificity. These results outperformed the known resistance sites for Amoxicillin (A1834G) and Clarithromycin (A2147), which had sensitivities of 22.2% and 87%, and specificities of 100% and 96%, respectively. Our study demonstrates that predictive modeling using supervised learning algorithms with feature selection can yield diagnostic models with higher predictive power compared to models relying on single single-nucleotide polymorphism (SNP) sites. This approach significantly contributes to enhancing the precision and effectiveness of antibiotic treatment strategies for Hp infections.”
BeijingPeople’s Republic of ChinaAsiaAntibacterialsAntibi- oticsAntimicrobialsCyborgsDrugs and TherapiesEmerging TechnologiesEpsilonproteobacteriaGe- neticsGram-Negative BacteriaHealth and MedicineHelicobacterHelicobacter pyloriMachine LearningParasitic Diseases and ConditionsPersonalized MedicinePersonalized TherapyProteobacteria
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